Systems and methods for clinical neuronavigation

ABSTRACT

Systems and methods for clinical neuronavigation in accordance with embodiments of the invention are illustrated. One embodiment includes a method for generating a brain stimulation target, including obtaining functional magnetic resonance imaging (fMRI) image data of a patient&#39;s brain, were brain imaging data describes neuronal activations within the patient&#39;s brain, determining a brain stimulation target by mapping at least one region of interest to the patient&#39;s brain, locating functional subregions within the at least one region of interest based on the fMRI image data, determining functional relationships between at least two brain regions of interest, generating parameters for each functional subregion, generating a target quality score for each functional subregion based on the parameters and selecting a brain stimulation target based on its target quality score and the patient&#39;s neurological condition.

CROSS REFERENCE TO RELATED APPLICATIONS

The present invention claims priority to U.S. Provisional PatentApplication Ser. No. 62/617,121 entitled “Systems and Methods forPersonalized Clinical Applications of Accelerated Theta-BurstStimulation” filed Jan. 12, 2018. U.S. Provisional Patent ApplicationSer. No. 62/617,121 is incorporated by reference in its entirety herein.

FIELD OF THE INVENTION

The present invention generally relates to generating personalizedtargets for invasive and non-invasive neuromodulation and implantablestimulation.

BACKGROUND

Transcranial Magnetic Stimulation (TMS) is a non-invasive medicalprocedure where strong magnetic fields are utilized to stimulatespecific areas of a patient's brain in order to treat a medicalcondition such as depression and neuropathic pain. Repeated applicationsof TMS in a short time frame is referred to as repetitive TMS (rTMS).Theta-burst stimulation (TBS) is a patterned form of rTMS, typicallyadministered as a triplet of stimuli with 20 ms between each stimuli inthe triplet, the triplet being repeated every 200 ms When TBS isperformed continuously, this results in cortical inhibition and istermed continuous theta burst stimulation (cTBS), and when doneintermittently with inter-train intervals between the triplets, this isexcitatory and termed intermittent theta-burst stimulation (iTBS)

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive imagingtechnique where neuronal activity is measured by tracking hemodynamicresponses in the brain. The resting state of a brain is the measurementof neuronal activity when the patient is not performing an explicittask. The resting state of a brain can be used to explore theconnectivity between various structures and regions in the brain. Acommon example of a resting state connectivity is the default modenetwork (DMN). A particular resting state connectivity between brainregions that share functional properties is called a Resting StateFunctional Connectivity (RSFC).

SUMMARY OF THE INVENTION

Systems and methods for clinical neuronavigation in accordance withembodiments of the invention are illustrated. One embodiment includes amethod for generating a brain stimulation target, including obtainingfunctional magnetic resonance imaging (fMRI) image data of a patient'sbrain, were brain imaging data describes neuronal activations within thepatient's brain, determining a brain stimulation target by mapping atleast one region of interest to the patient's brain, locating functionalsubregions within the at least one region of interest based on the fMRIimage data, determining functional relationships between at least twobrain regions of interest, generating parameters for each functionalsubregion, generating a target quality score for each functionalsubregion based on the parameters and selecting a brain stimulationtarget based on its target quality score and the patient's neurologicalcondition.

In another embodiment, the brain imaging data describes neuronalactivity during a resting state.

In a further embodiment, obtaining the brain imaging data furtherincludes preprocessing the brain imaging data.

In still another embodiment, preprocessing the brain imaging dataincludes performing at least one preprocessing step selected from thegroup consisting of physiological noise regression, slice-timecorrection, motion correction, co-registration, band-pass filtering, andde-trending.

In a still further embodiment, a brain atlas is used for mapping the atleast one region of interest onto an individual's brain anatomy.

In yet another embodiment, the each functional subregion describeshomogenous brain activity.

In a yet further embodiment, functional subregions are identified andseparated from each other using hierarchical agglomerative clustering.

In another additional embodiment, subregion parameters are selected fromthe group consisting of size of the functional subregion, concentrationof voxels that make up the functional subregion, the correlation betweenthe functional subregion and other functional subregions, andaccessibility of the functional subregion to a transcranial magneticstimulation device.

In a further additional embodiment, the target quality score reflects acombination of weighted parameters of each functional subregion, where ahigher quality score reflects a better brain stimulation target.

In another embodiment again, the surface influence for a given subregiona voxel number weighted combination of the Spearman correlationcoefficients derived from a hierarchical clustering algorithm describingthe correlation coefficients between a subregion on the surface of thebrain and all subregions located deep within the brain.

In a further embodiment again, the brain stimulation target is atranscranial magnetic stimulation target.

In still yet another embodiment, further including stimulating the brainstimulation target using a transcranial magnetic stimulation device inaccordance with an accelerated theta burst stimulation protocol.

In a still yet further embodiment, a system for generating a brainstimulation target, including a neuronavigation computing systemcomprising at least one processor and a memory containing aneuronavigation application, where the neuronavigation applicationdirects the processor to obtain brain imaging data from a magneticresonance imaging machine capable of obtaining functional magneticresonance imaging (fMRI) image data of a patient's brain, where thebrain imaging data describes neuronal activations within the patient'sbrain, map at least one region of interest to the patient's brain,locate functional subregions within the at least one region of interestbased on the fMRI image data, determine functional relationships betweenat least two functional subregions, generate subregion parameters foreach functional subregion, generate a target quality score for eachfunctional subregion based on the subregion parameters, and select abrain stimulation target based on its target quality score and thepatient's neurological condition.

In still another additional embodiment, the fMRI image data describesneuronal activity during a resting state.

In a still further additional embodiment, the neuronavigationapplication further directs the processor to preprocess the fMRI imagedata.

In still another embodiment again, to preprocess fMRI image data, theneuronavigation application further directs the processor to perform atleast one preprocessing step selected from the group consisting ofphysiological noise regression, slice-time correction, motioncorrection, co-registration, band-pass filtering, and de-trending.

In a still further embodiment again, a brain atlas is used to map the atleast one subregion.

In yet another additional embodiment, the each functional subregiondescribes homogenous brain activity.

In a yet further additional embodiment, functional subregions arelocated using hierarchical agglomerative clustering.

In yet another embodiment again, subregion parameters are selected fromthe group consisting of size of the functional subregion, concentrationof voxels that make up the functional subregion, the correlation betweenthe functional subregion and other functional subregions, andaccessibility of the subregion from the surface of the brain.

In a yet further embodiment again, the target quality score reflects thesurface influence for a given subregion.

In another additional embodiment again, the surface influence for agiven subregion is the sum of a two dimensional matrix of Spearmancorrelation coefficients derived from a hierarchical clusteringalgorithm describing the correlation coefficients between a subregion onthe surface of the brain and all subregions located deep within thebrain.

In a further additional embodiment again, the brain stimulation targetis a transcranial magnetic stimulation target.

In still yet another additional embodiment, the neuronavigationapplication further directs the processor to stimulate the brainstimulation target using a transcranial magnetic stimulation device inaccordance with an aTBS protocol.

Additional embodiments and features are set forth in part in thedescription that follows, and in part will become apparent to thoseskilled in the art upon examination of the specification or may belearned by the practice of the invention. A further understanding of thenature and advantages of the present invention may be realized byreference to the remaining portions of the specification and thedrawings, which forms a part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The description and claims will be more fully understood with referenceto the following figures and data graphs, which are presented asexemplary embodiments of the invention and should not be construed as acomplete recitation of the scope of the invention.

FIG. 1 is a diagram illustrating an accelerated Theta-Burst Stimulationsystem in accordance with an embodiment of the invention.

FIG. 2 is a diagram illustrating an accelerated Theta-Burst Stimulationcomputing system in accordance with an embodiment of the invention.

FIG. 3 is a flow chart illustrating a method for applying personalizedaccelerated intermittent Theta-Burst Stimulation to a patient inaccordance with an embodiment of the invention.

FIG. 4 is a stimulation schedule for accelerated intermittentTheta-Burst Stimulation in accordance with an embodiment of theinvention.

FIG. 5 is a stimulation schedule for accelerated continuous Theta-BurstStimulation in accordance with an embodiment of the invention.

FIG. 6 is a flow chart illustrating a method for generating apersonalized accelerated Theta-Burst Stimulation target in accordancewith an embodiment of the invention.

FIG. 7 is a set of charts illustrating a hierarchical agglomerativeclustering algorithm for identifying functional subregions in a regionof interest in accordance with an embodiment of the invention.

FIG. 8A is a chart of subregion correlation coefficients in accordancewith an embodiment of the invention.

FIG. 8B is a chart of ROI 1 functional subregion sizes in accordancewith an embodiment of the invention.

FIG. 8C is a chart of ROI 1 functional subregion voxel non-cohesion inaccordance with an embodiment of the invention.

FIG. 8D is a chart of net relationships between each ROI 1 subregion anall ROI 2 subregions in accordance with an embodiment of the invention.

FIG. 8E is a chart of subregion voxel concentration in accordance withan embodiment of the invention.

FIG. 8F is a chart of cluster quality in accordance with an embodimentof the invention.

DETAILED DESCRIPTION

The brain is a delicate organ, and so when medical professionals areperforming procedures, tests, or otherwise interfacing with its physicalstructures, precise targets can be very useful. For example, whenstimulating a particular portion of a brain in order to treat a patient,stimulating the wrong portion can not only result in failed orincomplete treatment, but may also create negative ramifications for thepatient. As such, systems and methods for neuronavigation which cangenerate and/or utilize precise targets for stimulation can reducefailure rates and increase the quality of treatment.

Turning now to the drawings, systems and methods for personalizedclinical applications of accelerated intermittent theta-burst magneticstimulation in accordance with embodiments of the invention areillustrated. rTMS has been accepted as an effective treatment forclinical depression by the U.S. Food and Drug Administration for nearlya decade and single daily application of iTBS (3 min, 600 pulses) hasbeen approved by the FDA recently. Recently, a new form of rTMS known asTheta-Burst Stimulation has been shown in its (excitatory) intermittentform (iTBS) to have an increase in efficiency over standard rTMS byreducing the amount of time the treatment needs to be applied to have atherapeutic effect. For example, 40 minutes of TMS (approximately 3000pulses) has been shown to be non-inferior to 3 minutes of iTBS(approximately 600 pulses). However, conventional rTMS/TBS treatmentregimens can take multiple weeks of sessions to achieve a stable andeffective reduction in clinical symptoms. Further, conventional rTMS andiTBS techniques have targeted portions of the brain based on averages oftotal activity within a region. Such techniques have failed to generatetargets optimized for a patient's individualized brain structure andconnectivity.

Accelerated Theta-Burst Stimulation (aTBS) is a new class of rTMSvariants described herein in which large quantities of TBS pulses areapplied over a short period to a targeted location within the brain.This can be accomplished to produce excitation (intermittent) or toproduce inhibition (continuous). Appropriate aTBS protocols inaccordance with embodiments of the invention can reduce time to clinicalimprovement to days from weeks compared to conventional rTMS/TBS meaningpatients can be hospitalized for shorter amounts of time.

Further, methods for performing aTBS can include generating personalizedaTBS targets for TBS stimulation which take in to account theidiosyncrasies of a patient. Personalized targets can be generated foraTBS to maximize efficiency and/or efficacy of treatment for eachpatient. Because TMS coils are currently incapable of targetingstructures deep within the brain, personalized aTBS targets can begenerated to stimulate surface regions that are linked to deep regions,bypassing the need for deep brain stimulation.

The importance of proper targeting is highlighted by the criticalsymptoms of chronic mental illness. For example, in persons sufferingfrom severe clinical depression, neurosurgery is considered a treatmentof last resort. If treatment by implanted stimulators fails, theincidence of suicide increases dramatically. By using neuronavigationtechniques described within, proper and effective targeting can beachieved prior to surgery to increase the success rate of the procedurein numerous ways. In many embodiments, aTBS is performed on a trialbasis to test the efficacy of a neuronal implant prior to surgery bystimulating a selected target or set of targets. In a variety ofembodiments, targets with the best test results are selected as targetsfor an implantable system. In the case that no target with a high enoughchance of success for the patient and/or medical professionals is found,then the invasive surgery can be avoided entirely. As magneticstimulation and electric stimulation are closely related, implantedneurostimulators can be programmed to provide electrical stimulationprotocols similar to aTBS magnetic protocols with similar results.Systems for performing aTBS and neuronavigation targeting methods arediscussed below.

aTBS Systems

aTBS systems in accordance with embodiments of the invention can acquireneuroimaging data of a patient's brain and generate personalized aTBStargets for treatment with aTBS devices. In many embodiments, aTBSsystems include aTBS devices. A conceptual diagram of an aTBS system inaccordance with an embodiment of the invention is shown in FIG. 1. aTBSsystem 100 includes an aTBS Device 110. aTBS devices can be any TMS coilthat is capable of delivering magnetic pulses with the frequencies,intensities, and durations required by aTBS. aTBS devices can be, butare not limited to, a Magventure X100 produced by MagVenture of Farum,Denmark, Magstim coils produced by The Magstim Company Limited ofWhitland, United Kingdom, Neurosoft coils produced by Neurosoft ofUtrecht, Netherlands, and the Brainsway H7-deep-TMS system, or the H1Coil TMS device, both of which are produced by Brainsway Ltd. ofJerusalem, Israel. However, any TMS coil can be used as appropriate tothe requirements of specific applications of embodiments of theinvention. Further, aTBS systems can include more than one aTBS devicein order to better target different regions of the brain.

In numerous embodiments, neuronavigation systems can be used tocorrectly place the aTBS device relative to the aTBS target.Neuronavigation systems capable of displaying the aTBS target caninclude Localite TMS Navigator produced by Localite GmbH of SanktAugustin, Germany, visor2 systems produced by ANT Neuro of theNetherlands, and BrainSite TMS Navigation produced by Rogue SolutionsLtd. of Cardiff, Wales. However, any kind of neuronavigation devicecapable of assisting with the placement of the aTBS device can beutilized as appropriate to the requirements of specific applications ofembodiments of the invention. In numerous embodiments, theneuronavigation system is provided and/or generates a target or set oftargets generated using targeting processes described below.

aTBS system 100 further includes an aTBS computing system 120 and abrain imaging device 130. aTBS computing systems can be implemented asone or more computing devices capable of processing brain image data andgenerating personalized aTBS targets. Brain imaging devices are capableof obtaining imaging data describing a patient's brain. Brain imagingdevices can obtain structural, functional imaging data, and/or restingstate imaging data. In numerous embodiments, brain imaging devices aremagnetic resonance imaging (MRI) machines, functional MRI machines(fMRI), or any other brain scanning device as appropriate to therequirements of specific applications of embodiments of the invention.Functional imaging data can be obtained by scanning a patient using anfMRI scanner or other brain imaging device, while the patient isperforming specific tasks and/or is provided specific stimuli. Restingstate imaging data can be obtained by scanning a patient using an fMRIscanner, or other brain imaging device while the patient is notperforming any task.

Additionally, aTBS system 100 includes interface device 140. Interfacedevices can be, but are not limited to, computers, smart-phones, tabletcomputers, smart-watches, or any other type of computing interfacedevice as appropriate to the requirements of specific applications ofembodiments of the invention. In many embodiments, interface devices areused to interface with brain imaging devices, aTBS computing systems,and/or aTBS devices. In numerous embodiments, aTBS computing systems andinterface devices are implemented using the same physical device. aTBSsystem 100 includes a network 150 connecting aTBS device 110, aTBScomputing system 120, and interface device 130. In a variety ofembodiments, the network is the Internet. However, any network such as,but not limited to, an intranet, a local area network, a wire areanetwork, or any other computing network capable of connecting computingdevices can be used as appropriate to the requirements of a givenapplication.

While a specific aTBS system is illustrated with respect to FIG. 1, oneof ordinary skill in the art would appreciate that numerous differentconfigurations of aTBS systems are possible, including, but not limitedto, system architectures where different devices are not connected, ornot all connected, by a network. aTBS computing systems are discussedbelow.

aTBS Computing Systems

aTBS computing systems in accordance with embodiments of the inventioncan generate personalized aTBS targets for a given patient. A conceptualillustration of an aTBS computing system in accordance with anembodiment of the invention is shown in FIG. 2. aTBS computing system200 includes a processor 210 in communication with a communicationsinterface 220 and a memory 230. In numerous embodiments, aTBS computingsystems include multiple processors, multiple memories, and/or multiplecommunications interfaces. In a variety of embodiments, components ofaTBS computing systems are distributed across multiple hardwareplatforms.

Processor 210 can be any type of computational processing unit,including, but not limited to, microprocessors, central processingunits, graphical processing units, parallel processing engines, or anyother type of processor as appropriate to the requirements of specificapplications of embodiments of the invention. Communications interface220 can be utilized to transmit and receive data from other aTBScomputing systems, brain imaging devices, aTBS devices, and/or interfacedevices. Communications interfaces can include multiple ports and/orcommunications technologies in order to communication with variousdevices as appropriate to the requirements of specific applications ofembodiments of the invention.

Memory 230 can be implemented using any combination of volatile and/ornon-volatile memory, including, but not limited to, random accessmemory, read-only memory, hard disk drives, solid-state drives, flashmemory, or any other memory format as appropriate to the requirements ofspecific applications of embodiments of the invention. In numerousembodiments, the memory 230 stores a variety of data, including, but notlimited to, an aTBS targeting application 232 and imaging data 234. Inmany embodiments, the aTBS targeting application and/or the imaging dataare received via the communications interface. Processor 210 can bedirected by the aTBS targeting application to perform a variety of aTBSprocesses, including, but not limited to, processing imaging data andgenerating aTBS targets.

Although specific architectures for aTBS computing systems in accordancewith embodiments of the invention are conceptually illustrated in FIG.2, any of a variety of architectures, including, but limited to, thosethat direct aTBS devices to perform aTBS, direct brain imaging devicesto capture imaging data, and/or utilize different hardware capable ofperforming similarly to the above can also be utilized. Furthermore,aTBS computing systems can be implemented on multiple servers within atleast one computing system. For example, aTBS computing systems can beimplemented on various remote “cloud” computing systems as appropriateto the requirements of specific applications of embodiments of theinvention. However, one of ordinary skill in the art would appreciatethat a “computing system” can be implemented on any appropriatecomputing device, including, but not limited to, a personal computer, aserver, a cluster of computing devices, and/or a computing deviceincorporated into a medical device. In numerous embodiments, aTBScomputing systems are implemented as part of an integrated aTBS device.A discussion of various aTBS processes is found below.

Processes for Performing aTBS

Traditional rTMS procedures utilize large numbers of pulses over a longperiod of time. Traditional TBS utilizes patterned pulses which reducethe number of pulses required to achieve similar results to rTMS.However, neither method can produce clinically useful changes in under aweek. Processes for performing aTBS in accordance with embodiments ofthe invention can include accelerated treatment regimens compared toconventional rTMS/TBS methods.

Turning now to FIG. 3, a process for performing aTBS in accordance withan embodiment of the invention is illustrated. Process 300 includesobtaining (310) patient brain imaging data. In numerous embodiments,patient brain imaging data includes anatomical imaging data, restingstate imaging data, functional imaging data, and/or any other brainimaging data as appropriate to the requirements of specific applicationsof embodiments of the invention. Anatomical imaging data can include,but is not limited to, structural MRI scan data, diffusion tensorimaging data, computed tomography scans, and/or any other structuralimage of a brain produced by an imaging technology. Functional imagingdata is imaging data that describes the neuronal activation of a brain.Resting state imaging data is imaging data that describes the neuronalactivation of a brain during a resting state. In numerous embodiments,functional imaging data and resting state imaging data are obtained fromfMRI scans.

Patient brain imaging data can be used to generate (320) personalizedaTBS targets. aTBS can be applied (330) to the patient at thepersonalized aTBS target. In many embodiments, the TMS coil utilized toapply the aTBS treatment is focused on the target by manipulating theplacement of coil and/or the coil angle, and thus the orientation of themagnetic field. In many embodiments, aTBS is applied according to apre-determined protocol. aTBS protocols can vary depending on numerousfactors, including, but not limited to, the severity of condition,whether or not aiTBS or acTBS is used, or any other factors asappropriate to the requirements of specific applications of embodimentsof the invention. aTBS protocols can include a set of parametersdescribing the form of iTBS to be applied, and a schedule describingwhen the iTBS is applied. In numerous embodiments, include e-fieldmeasurements are used to inform the choice of coil angle.

In numerous embodiments, aiTBS schedules involves applying iTBS pulsesfor multiple sessions per day, for several days. In a variety ofembodiments, the iTBS pulse parameters involve 3-pulse, 50 Hz pulses at5 Hz for 2 second trains, with trains every 10 seconds for 10 minutesessions (1,800 total pulses per session). In many embodiments, aiTBSschedules describe conducting 10 sessions per day with 50-minuteintersession intervals for 5 consecutive days (18,000 pulses per day,90,000 total pulses).

However, a wide range of parameters can be used, for example, iTBS pulseparameters can involve any number of pulses of between 20 Hz and 70 Hz,at 3 Hz to 7 Hz, with trains every 4 seconds to 10 seconds, withintersession intervals between 25 minutes and 120 minutes. An exampleschedule for treatment using aiTBS in accordance with an embodiment ofthe invention is illustrated in FIG. 4.

In a variety of embodiments, the cTBS pulse parameters involve 3-pulsetrains with 50 Hz pulses at 5 Hz for 40 second sessions (600 totalpulses per session). In a variety of embodiments, the cTBS pulseparameters involve 3-pulse, 30 Hz pulses at 6 Hz for 44 second sessions(800 total pulses per session). In many acTBS embodiments, 30 sessionsare applied per day with 15-minute intersession intervals for 5consecutive days (18,000 pulses per day, 90,000 total pulses). However,a wide range of parameters can be used, for example, cTBS parameters caninvolve any number of pulses of 20 Hz to 70 Hz, at 3 Hz to 7 Hz with anintersession interval of between 10 and 50 minutes. An example schedulefor treatment using acTBS in accordance with an embodiment of theinvention is illustrated in FIG. 5.

However, TBS parameters and schedules for an aTBS protocol can bevaried. For example, the number of pulses or frequency of sessions canbe increased or decreased depending on the refractoriness of the patientand/or the severity of the clinical condition (i.e. for less severecases of depression, fewer pulses can be effectively utilized, therebyfurther reducing treatment times). In numerous embodiments, the numberof pulses per session range from approximately 600 to 2,400 depending onthe type of aTBS applied. In a variety of embodiments, the number ofsessions for aiTBS range from 3 to 15 sessions per day. In manyembodiments, the number of sessions for acTBS range from 10-40 sessionsper day. Nonetheless, one of ordinary skill in the art would appreciatethat any number of pulses and lengths of intersession intervals can beused as appropriate to the requirements of specific applications ofembodiments of the invention to fit the needs of an individual patient.

Changes in the patient's resting state can be measured (340) usingmethods similar to those described above with respect to obtainingresting state imaging data. If there is sufficient change (350) inresting state to result in the clinical results desired, then treatmentcan optionally end. If there has not been a sufficient change (350) inthe patient's resting state, additional aTBS treatment sessions can beperformed.

Specific processes for performing aTBS in accordance with embodiments ofthe invention are described above and shown with respect to FIG. 3;however, any number of processes, including, but not limited to, thosethat use alternate number of pulses, sessions, frequencies, imagingmethods, and/or degree of change in resting state, can be utilized asappropriate to the requirements of a specific application in accordancewith embodiments of the invention.

Generating aTBS Targets

Every person has a unique brain structure and connectivity. While theaverage across all brains has been used to generate targets, thisignores each patient's idiosyncrasies. Processes for performing aTBS inaccordance with embodiments of the invention can include generatingpersonalized aTBS targets to increase the efficiency and efficacy overstandard iTBS treatment. Turning now to FIG. 6, a process for generatingan aTBS target in accordance with an embodiment of the invention isillustrated.

Process 600 includes obtaining (610) brain imaging data. In manyembodiments, brain imaging data includes structural imaging data,resting state imaging data, and/or functional imaging data similar tothose described above. In numerous embodiments, brain imaging data ispreprocessed. Preprocessing steps can include, but are not limited to,physiological noise regression, slice-time correction, motioncorrection, co-registration, band-pass filtering, de-trending, and/orany other preprocessing step as appropriate to the requirements of agiven application. Brain imaging data can be used to map (620)standardized regions of interest (ROIs) onto the individualized anatomy.In numerous embodiments, mapping standardized regions of interest ontoindividualized anatomy is performed by aligning structural imaging datato a standardized brain atlas, and reversing the alignment parameters tomap a standardized ROI onto an individual's anatomy. Standardized brainatlases can define one or more brain regions that are targetable withTMS. In a variety of embodiments, the brain atlas defines brain regionsthat are targetable with a specific TMS coil. In numerous embodiments,task-based fMRI is used to restrict and/or broaden the extent of thebrain regions that are to be targeted with TMS.

In many embodiments, a personalized map of functional activations withinROIs is generated (630). In a variety of embodiments, functionalactivations within ROIs are identified using functional imaging dataand/or resting state imaging data. The map of individualized ROIs andresting state imaging data can be used to generate (640) a personalizedmap of functional subregions within ROIs. In numerous embodiments,functional subregion is defined as a brain region within ROIs in whichall temporal brain activity is highly correlated across the spatialextent of the subregion. In many embodiments, the resting state imagingdata can be used to generate resting state functional connectivity datadescribing the functional connectivities between regions of the brain.In numerous embodiments, resting state data extracted from the restingstate imaging data such as, but not limited to, functionalconnectivities and task based neuronal activations can be mapped to thepersonalized regions of interest. In numerous embodiments, apersonalized map of task based functional activations within the ROIscan be utilized to refine or expand a functional connectivity basedpersonalized map of functional subregions. Task-based functional imagingdata can lead to more refined and/or higher quality analysis offunctional connectivities between subregions.

Additionally, resting state functional connectivity data across ROIs canbe further parcellated into functional subregions such that eachparcellated subregion is made up of homogeneous brain activity asmeasured over the course of a resting state scan. In numerousembodiments, parcellation is achieved using hierarchical clustering,such as, but not limited to, hierarchical agglomerative clustering.However, any parcellation method can be used as appropriate to therequirements of a given embodiment. In a variety of embodiments, thesize of a subregion is determined by the number of voxels that act in ahomogenous fashion. As such, the size of a subregion can vary acrossdifferent ROIs depending on the function of the subregion, the structureof the brain in which the subregion is located, the idiosyncrasies ofthe patient's brain, and any of a number of other factors that impactthe connectivity and reactivity of the brain.

The relationships between the functional subregions can be determined(650) using a variety of techniques. In numerous embodiments, the voxeltime course that is most highly correlated with the median of all voxeltime courses within a functional subregion can be selected as reflectingthe activity of the functional subregion. In many embodiments, taking asimple average of all time courses for voxels within the subregion canbe used, although this tends to be less robust. By reducing eachfunctional subregion to a single time course, an accurate representationof the typical time course of brain activity that occurs within groupsof homogenous voxels can be determined.

An example grouping and correlation calculation in accordance with anembodiment of the invention is illustrated in FIG. 7. Further, single,representative time courses enable the calculation of correlationcoefficients between functional subregions that exist within the sameROI. However, these correlation coefficients will tend to be low becausegroups of voxels with high correlation coefficients tend to be organizedinto the same subregion. The process of reducing each functionalsubregion identified through parcellation to a single time coursefurther allows for the calculation of correlation coefficients betweenall functional subregions discovered across multiple ROIs across thebrain. For example, multiple subregions within the left dorsolateralprefrontal cortex can be correlated with multiple subregions within thecingulate cortex, although any number of different subregions within anynumber of different brain structures can be correlated. An example setof correlations across multiple ROIs in accordance with an embodiment ofthe invention is illustrated in FIGS. 8A-8F.

Process 600 further includes parameterizing (660) functional subregions.Subregions can be assigned values for various parameters such as, butnot limited to, size of the subregion i.e. the number of voxels orvolume of brain, the spatial concentration of voxels i.e. the number ofvoxels divided by the average Euclidean 3 dimensional spatial distancebetween all voxels that make up the subregion, the functionalrelationships between a given subregion and other functional subregions(i.e. voxel-weighted average correlation coefficients), and/oraccessibility of the subregion from the surface of the brain such as,but not limited to, the depth of the subregion and/or whether or not thesubregion is obscured by another subregion. However, any number ofparameters, or parameter weighting schemes can be used as appropriate tothe requirements of a given application.

Functional subregion parameters can be used to calculate (670) targetquality scores for each the functional subregions. In numerousembodiments, the target quality score for each subregion is a functionof weighted combinations of subregion parameters. In many embodiments,the target quality scores are generated by determining the surfaceinfluence (voxel size-weighted correlation coefficients) for a set ofsubregions. In many embodiments, the surface influence for a givensubregion is the sum of a two-dimensional matrix of Spearman's orPearson's correlation coefficients derived from a hierarchicalclustering algorithm describing the correlation coefficients between asurface ROI subregion and all of the deep ROI subregions. In numerousembodiments, the first ROI is located near the surface of the brain, andthe second ROI is located deeper within the brain tissue. However,difference in depth is not a requirement for ROI comparisons. SurfaceROI subregions that have positive influence on deep subregions andsurface ROI subregions that have negative influence on deep subregionscan be determined based on the surface influence calculations. In someembodiments, depending on the type of treatment, only positive or onlynegative influencing subregions may be further considered as targets. Innumerous embodiments, the surface subregion concentration can becalculated. In many embodiments, surface subregion concentration can becalculated by dividing the number of voxels in a surface subregion bythe surface subregion non-cohesion as measured by the average Euclidiandistance between voxels in the subregion. The surface subregionconcentration can be further normalized to assist in interpretation. Innumerous embodiments, target quality scores are determined bymultiplying the surface subregion concentration by the surface influenceof a particular subregion. As such, target quality scores can reflectboth the potential impact of stimulating a given surface subregion on adeep subregion as well as the ability and efficiency with which thesubregion can be targeted by the TMS coil. However, any number ofmethods can be used to generate target quality scores as appropriate tothe requirements of specific applications of embodiments of theinvention.

Personalized aTBS target can be generated (680) based on the targetquality scores. In numerous embodiments, the highest quality targets areselected as the personalized aTBS target. In a variety of embodiments,more than one target can be selected.

Specific processes for generating aTBS targets in accordance withembodiments of the invention are described above and shown with respectto FIG. 6; however, any number of processes, including, but not limitedto, those that use different and/or fewer types of imaging data, usedifferent subregion parameters, different parcellation methods, and/orany other quality generation method can be utilized as appropriate tothe requirements of a specific application in accordance withembodiments of the invention.

Clinical Treatment Using aTBS

Clinical evaluation as determined that aTBS can be used to treat avariety of different medical conditions, both mental and physical. Forexample, aiTBS is effective at reducing suicide ideation and lesseningsymptoms of depression when performed over the left dorsolateralprefrontal cortex (L-DLPFC). Patients undergoing aiTBS over L-DLPFCtreatment are often able to be released from hospitalization in between2 and 5 days. Further, aiTBS over L-DLPFC can be used to increase heartrate variability, which is correlated with numerous psychiatricconditions as well as various types of cancer, various heart diseases,and inflammatory conditions.

In a variety of embodiments, heart rate variability is treated bytargeting the L-DLPFC in such a way as to effect changes in theSubcallosal Cingulate Cortex (SCC) which in turn can effect changes inthe vagus nerve which can be used to effect heart rate. Heart ratevariability can be used as a neurophysiological biomarker that istreatment-responsive for resolution of prefrontal dysregulation ofsympathetic/parasympathetic balance. For example, in many embodiments,heart rate decelerations are used to confirm target engagement.Similarly, ongoing recording of heart rate decelerations can be used forongoing target confirmation. Identified functional subregions can beused to find the best available targets for influencing heart ratevariability. This can be done by iteratively testing each functionalsubregion with in an ROI TMS stimulation for its effects on heart rateor heart rate variability.Implantable aTBS

In numerous embodiments, for severe clinical conditions, aTBS can beapplied by implanted neurostimulators. aTBS protocols can be adapted toelectrical stimulation instead of magnetic stimulation, and stimuli canbe applied long-term without the need for external magnetic stimulation.Further, external aTBS with an rTMS device can be performed to probe forthe correct target choice and therapeutic efficacy, and thenneurostimulators can be implanted over the target determined to have themost likely positive results either epidurally or subdurally. Innumerous embodiments, neurostimulators provide stimulation and recordbrain activity. By providing both stimulation and recording channels, aclosed loop system can be achieved. Based on recorded brain activity,stimulation can be modulated to either increase or decrease the amountof aTBS provided or change stimulation target. Further, machine learningalgorithms can be used to adapt to the optimal stimulation strategy forthat person. For example, if positive neurological activity is recorded,stimulation can cease until pathological neurological activity isdetected. In numerous embodiments, recording brain activity is achievedusing standard electrocortiocographical (ECoG) methods.

In a variety of embodiments, multiple stimulation electrodes can beimplanted over multiple targets, either for aTBS applications orotherwise. Recording activity can be used to selectively activate ordeactivate different electrodes in response to different responses forsafety reasons as well as clinical as for aTBS applications. Forexample, if a seizure activity is detected in a particular area of thebrain, stimulation electrodes in that region can be deactivated and/orutilized to induce normal brain activity.

Neurostimulators can also be used with open loop parameters, where astatic protocol is utilized to maintain a desired brain activationpattern. In many embodiments, the static protocol can be adapted ormodified via an external controller. Many neurostimulator devices use acombination of open loop and/or closed looped formats as appropriate tothe requirements of a given application of an embodiment of theinvention.

As noted above, neurostimulators can be placed on targets selected usingneuronavigation techniques above. However, when the surgeon is placingthe neurostimulator, it can be useful to verify placement during theprocedure. In numerous embodiments, organ responses to brain stimulationcan be used to verify placement. For example, an electrode placed on theL-DLPFC can affect heart rate, and by measuring heart rate, stimulationof the L-DLPFC can be verified. As L-DLPFC stimulation can also effectdepression, this can be a useful clinical tool for implantation ofneurostimulators to treat clinical depression.

Electrocorticography (epidural/subdural), EEG, and NIRS can becorrelated with heart rate and heart rate variability, and both can beused as closed loop indicators of ongoing efficacious stimulation.

Further, as aTBS is able to be performed to have an excitatory (aiTBS)or an inhibitory (acTBS) effect, with correct target selection, variousneurological changes can be implemented that can have impacts beyond thebrain. For example, manipulating the hypothalamus can impact cortisolsecretion by altering the hypothalamic-pituitary-adrenal axis.Additionally, post-injury, neurological plasticity can be increased byexciting and inhibiting different neuronal connections. Similarly, aTBScan be used to promote learning and skill acquisition by increasingneurological plasticity directly in targeted neuronal networks orelsewhere.

Although specific systems and methods for performing aTBS are discussedabove, many different systems and methods can be implemented inaccordance with many different embodiments of the invention. It istherefore to be understood that the present invention may be practicedin ways other than specifically described, without departing from thescope and spirit of the present invention. Thus, embodiments of thepresent invention should be considered in all respects as illustrativeand not restrictive. Accordingly, the scope of the invention should bedetermined not by the embodiments illustrated, but by the appendedclaims and their equivalents. U.S. patent application Ser. No.16/215,512 titled “Systems and Methods for Personalized ClinicalApplications of Accelerated Theta-Burst Stimulation” filed Dec. 10,2018, and U.S. patent application Ser. No. 16/215,519 titled “Systemsand Methods for Personalized Treatment of Neurological Conditions usingImplantable Neurostimulators” filed Dec. 10, 2018 are incorporated byreference in their entireties herein for all purposes.

What is claimed is:
 1. A method for generating a brain stimulationtarget, comprising: generating functional magnetic resonance imaging(fMRI) image data of a patient's brain using a magnetic resonanceimaging machine, wherein the fMRI image data describes neuronalactivations within the patient's brain; determining a brain stimulationtarget using a neuronavigation computing system by: mapping at least oneregion of interest to the patient's brain; locating functionalsubregions within the at least one region of interest based on the fMRIimage data; determining functional relationships between at least two ofthe functional subregions; generating parameters for each functionalsubregion; generating a target quality score for each functionalsubregion based on the parameters; and selecting a brain stimulationtarget based on the target quality scores and a neurological conditionof the patient.
 2. The method of claim 1, wherein the brain imaging datadescribes neuronal activity during a resting state.
 3. The method ofclaim 1, wherein generating the brain imaging data further comprisespreprocessing the brain imaging data.
 4. The method of claim 3, whereinpreprocessing the brain imaging data comprises performing at least onepreprocessing step selected from the group consisting of physiologicalnoise regression, slice-time correction, motion correction,co-registration, band-pass filtering, and de-trending.
 5. The method ofclaim 1, wherein a brain atlas is used for mapping the at least oneregion of interest onto the patient's brain anatomy.
 6. The method ofclaim 1, wherein each functional subregion describes homogenous brainactivity.
 7. The method of claim 1, wherein the functional subregionsare identified and separated from each other using hierarchicalagglomerative clustering.
 8. The method of claim 1, wherein theparameters for each functional subregion are selected from the groupconsisting of size of the functional subregion, concentration of voxelsthat make up the functional subregion, a correlation between thefunctional subregion and other functional subregions, and accessibilityof the functional subregion to a transcranial magnetic stimulationdevice.
 9. The method of claim 1, wherein the target quality scorereflects a combination of weighted parameters of each functionalsubregion, where a higher quality score reflects a better brainstimulation target.
 10. The method of claim 9, wherein the brainstimulation target is one of the functional subregions, where the brainstimulation target is located in a surface region of the brain, and thetarget quality score is further based on a surface influence metric forthe brain stimulation target comprising a number weighted combination ofSpearman correlation coefficients derived from a hierarchical clusteringalgorithm describing correlation coefficients between the brainstimulation target and all of the functional subregions located in adeep region of the brain.
 11. The method of claim 1, where the brainstimulation target is a transcranial magnetic stimulation target. 12.The method of claim 1, further comprising stimulating the brainstimulation target using a transcranial magnetic stimulation device inaccordance with an aTBS protocol.
 13. A system for generating a brainstimulation target, comprising: a neuronavigation computing systemcomprising at least one processor and a memory containing aneuronavigation application, where the neuronavigation applicationdirects the processor to: obtain brain imaging data from a magneticresonance imaging machine capable of obtaining functional magneticresonance imaging (fMRI) image data of a patient's brain, where thebrain imaging data describes neuronal activations within the patient'sbrain; map at least one region of interest to the patient's brain;locate functional subregions within the at least one region of interestbased on the fMRI image data; determine functional relationships betweenat least two of the functional subregions; generate subregion parametersfor each functional subregion; generate a target quality score for eachfunctional subregion based on the subregion parameters; and select abrain stimulation target based on the target quality scores and aneurological condition of the patient.
 14. The system of claim 13,wherein the fMRI image data describes neuronal activity during a restingstate.
 15. The system of claim 13, wherein the neuronavigationapplication further directs the processor to preprocess the fMRI imagedata.
 16. The system of claim 15, wherein to preprocess the fMRI imagedata, the neuronavigation application further directs the processor toperform at least one preprocessing step selected from the groupconsisting of physiological noise regression, slice-time correction,motion correction, co-registration, band-pass filtering, andde-trending.
 17. The system of claim 13, wherein a brain atlas is usedto map at least one of the functional subregions.
 18. The system ofclaim 13, wherein each functional subregion describes homogenous brainactivity.
 19. The system of claim 13, wherein the functional subregionsare located using hierarchical agglomerative clustering.
 20. The systemof claim 13, wherein the subregion parameters are selected from thegroup consisting of size of the functional subregion, concentration ofvoxels that make up the functional subregion, a correlation between thefunctional subregion and other functional subregions, and accessibilityof the subregion from the surface of the brain.
 21. The system of claim13, wherein the target quality score reflects a surface influence metricfor a given subregion.
 22. The system of claim 21, wherein the surfaceinfluence for a given subregion is the sum of a two dimensional matrixof Spearman correlation coefficients derived from a hierarchicalclustering algorithm describing the correlation coefficients between oneof the functional subregions selected as the brain stimulation target,where the selected functional subregion is located in a surface regionof the brain, and all functional subregions located in a deep region ofthe brain.
 23. The system of claim 21, wherein the brain stimulationtarget is a transcranial magnetic stimulation target.
 24. The system ofclaim 21, wherein the neuronavigation application further directs theprocessor to stimulate the brain stimulation target using a transcranialmagnetic stimulation device in accordance with an aTBS protocol.